Semi-supervised Graph Embedding Approach to Dynamic Link Prediction
This work addresses link prediction in dynamic networks, which is important for applications like social network analysis, but it appears incremental as it builds on existing embedding methods with specific enhancements.
The paper tackles dynamic link prediction in networks by proposing a semi-supervised graph embedding approach that learns embeddings from both temporal and cross-sectional structures, achieving performance comparable to state-of-the-art methods in link formation prediction and outperforming them in link dissolution prediction on three real-world datasets.
We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contributes to the predictive performance of our model and we provide experiments with three real--world dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state of the art baseline methods in link dissolution prediction.